Deep Tasks Summarization for Comprehending Mixed Tasks in a Commit

Abstract

In Version Control System (VCS), a developer frequently uploads multiple tasks such as adding features, code refactoring, and fixing bugs, into a single commit and crumbles each task’s summary when writing a commit message. It causes code readers to feel challenged in understanding the developer’s past tasks within the commit history. To resolve this issue, we propose an automatic approach to generating a task summary to help comprehend multiple mixed tasks in a commit and developed tool support named Task summary Generator (TsGen). In our approach, we use the commit with a single task as input and identify the task to sort its elements sequentially. Then we generate feature vectors from each sorted element to train the Neural Machine Translation (NMT) model. Based on the trained NMT model, we generate the feature vector from each task of a commit with multiple tasks and put each of them into the model to provide the task summary. In evaluation, we compared the performance of TsGen with two existing methods for nine open-source projects. As a result, TsGen outperformed CoDiSum and Jiang’s NMT by 52.08% and 28.07% in BiLingual Evaluation Understudy (BLEU) scores. In addition, the human evaluation was carried out to demonstrate that TsGen helps understand mixed tasks in a commit and gained a 0.27 higher preference than the actual commit message.